diff --git a/movement/io/load_poses.py b/movement/io/load_poses.py
index 745f64c1..67da754a 100644
--- a/movement/io/load_poses.py
+++ b/movement/io/load_poses.py
@@ -696,3 +696,67 @@ def _ds_from_valid_data(data: ValidPosesDataset) -> xr.Dataset:
             "ds_type": "poses",
         },
     )
+
+
+def from_anipose_df(anipose_triangulation_df, individual_name="individual_0"):
+    """Convert triangulation dataframe to xarray dataset.
+    Reshape dataframe with columns keypoint1_x, keypoint1_y, keypoint1_z, keypoint1_confidence_score, 
+    keypoint2_x, keypoint2_y, keypoint2_z, keypoint2_confidence_score, ...
+    to array of positions with dimensions time, individuals, keypoints, space,
+    and array of confidence scores with dimensions time, individuals, keypoints
+
+    Parameters
+    ----------
+    anipose_triangulation_df : pd.DataFrame
+        Anipose triangulation dataframe
+    individual_name : str, optional
+        Name of the individual, by default "individual_0"
+
+    Returns
+    -------
+    xarray.Dataset
+        ``movement`` dataset containing the pose tracks, confidence scores,
+        and associated metadata.
+    """
+    keypoint_names = sorted(list(set([col.rsplit('_', 1)[0] for col in anipose_triangulation_df.columns 
+                                   if any(col.endswith(f'_{s}') for s in ['x','y','z'])])))
+
+    n_frames = len(anipose_triangulation_df)
+    n_keypoints = len(keypoint_names)
+
+    # Initialize arrays and fill
+    position_array = np.zeros((n_frames, 1, n_keypoints, 3))  # 1 for single individual
+    confidence_array = np.zeros((n_frames, 1, n_keypoints))
+    for i, kp in enumerate(keypoint_names):
+        for j, coord in enumerate(['x', 'y', 'z']):
+            position_array[:, 0, i, j] = anipose_triangulation_df[f'{kp}_{coord}']
+        confidence_array[:, 0, i] = anipose_triangulation_df[f'{kp}_score']
+
+    individual_names = [individual_name]
+
+    return from_numpy(position_array=position_array,
+                      confidence_array=confidence_array, 
+                      individual_names=individual_names,
+                      keypoint_names=keypoint_names,
+                      source_software="anipose_triangulation")
+
+
+def from_anipose_csv(anipose_csv_path, individual_name="individual_0"):
+    """Convert anipose csv to xarray dataset.
+
+    Parameters
+    ----------
+    anipose_csv_path : pathlib.Path
+        Path to the Anipose triangulation csv file
+    individual_name : str, optional
+        Name of the individual, by default "individual_0"
+
+    Returns
+    -------
+    xarray.Dataset
+        ``movement`` dataset containing the pose tracks, confidence scores,
+        and associated metadata.
+    """
+    anipose_triangulation_df = pd.read_csv(anipose_csv_path)
+    # TODO add a validator for the anipose csv file at this level?
+    return from_anipose_df(anipose_triangulation_df, individual_name)